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Lan Wang, Douglas T. Hall, and Lea Waters
Contents:
  1. Segmentation analysis of financial savings markets through the lens of psycho-demographics
  2. Saving for Retirement: Intention, Context and Behavior - Semantic Scholar
  3. Kundrecensioner
  4. Acta Commercii
  5. Are You Getting the Benefit of Your Employee Benefits?

In our review, they include job control, opportunities to develop, skill discretion, recognition at work, work variety and greater social cohesion. Job control is by far the most studied job resource, and consequently, it has been operationalised in many different ways. Job strain was defined as the ratio of job demands to job control or in some cases, the difference between job demands and job control. There was only one analysis of job strain, and it was included under job demands in this review.

Job satisfaction included professional satisfaction, career satisfaction, work enjoyment, challenge at work, meaningfulness of work, reward in work, work providing active interest and job content plateau. In this review, we treated it as an example of job dissatisfaction. Social support was defined differently by almost every study which included it. This heterogeneity made the summary table less informative for this work characteristic, so that table has been moved to the online supplementary materials.

The following were all categorised as measures of social support in this review: support from co-workers, support from supervisors, quality of leadership, team-working and perceived support from supervisors for working till age The following were taken to be negative workplace characteristics indicative of low social support: exposure to bullying, conflicts in work and perceived pressure from colleagues to retire early.

Organisational resources included operationalisations of organisational support, organisational justice, management quality and organisational stimulation. Organisational injustice was treated as an operationalisation of a lack of organisational justice and therefore as an absence of an organisational resource. There were some characteristics that did not feature in enough analyses to permit conclusions to be drawn about them. These characteristics feature in the summary tables, but they are not presented in their own tables in the main text see supplementary online material for tables of these characteristics.

Effort — reward imbalance was operationalised, in all studies, as the imbalance between effort expended at work including commitment to work and rewards received in terms of being valued at work, salary, promotion prospects and job security. The associations between psychosocial work characteristics and retirement intention and actual retirement are presented based on the direction of evidence.

Summary of evidence for association of psychosocial work characteristics with retirement timing. Direction of evidence for analyses of job resources in relation to retirement timing. Where studies defined job resources in more detail, they appear on other rows of the table.

Other subtypes of resources were not analysed enough times for us to draw conclusions. Direction of evidence for analyses of job demands in relation to retirement timing. Direction of evidence for analyses of job satisfaction in relation to retirement timing. There were 17 analyses of social support. Overall, the evidence was moderately in favour of the view that social support promotes later retirement.

This makes it difficult to draw conclusions about subtypes of social support. There were two analyses of organisational resources in relation to actual retirement, one of which found greater organisational resources to be associated with later actual retirement Thorsen et al. Organisational justice was the only subtype of organisational resources to be analysed in more than one paper. As most subtypes of organisational resources appeared only in a single paper, it is not possible to draw conclusions about subtypes of organisational resources.

There were two analyses for retirement intentions, both of which found high ERI to be associated with earlier retirement intentions Wahrendorf et al. Three analyses considered actual retirement, with just one finding high ERI to be associated with later actual retirement Hintsa et al. For retirement intentions, one analysis out of three found high job insecurity to be associated with intended earlier retirement Stynen et al.

There was only one analysis of job insecurity in relation to actual retirement, and it produced a null outcome Lund and Villadsen We performed a systematic review of evidence from 46 papers. There were sufficient analyses of job resources 38 papers , job demands 30 papers , job satisfaction 19 papers and social support 17 papers to draw conclusions about their effects on retirement.

There was insufficient evidence about effort—reward imbalance 5 papers , job strain 1 paper or job insecurity 3 papers to draw conclusions about these workplace characteristics. For employees who are happy with their current level of development, the idea of further training might be daunting and may even precipitate earlier retirement. There was moderate evidence for the association between higher social support and extended working.

There was less evidence regarding organisational resources and retirement six papers. By contrast, there was limited evidence for the association of job demands with retirement. Perceived job demands may influence retirement intentions without manifesting in actual retirement—for example if individuals are unable to retire due to financial circumstances. Another reason for the mixed findings could be that high demands are often associated with higher-status jobs, which tend also to be accompanied by higher job resources Hintsa et al.

This review suggests that high-quality psychosocial working conditions may encourage later retirement. To retain older employees in the workforce, employers should seek to increase job resources, especially job control. This should include possibilities for flexible working, such as job sharing, self-rostering, working from home and split shifts PRIME Flexible working might also help older workers with caregiving responsibilities. A greater emphasis on providing new learning opportunities tailored to older employees and greater valuing of the contribution of older employees would also promote extended working de Wind et al.

To our knowledge, this is the first systematic review of the evidence on psychosocial work characteristics in relation to retirement. It drew upon a systematic review of high-quality studies, most published within the past decade. The recency of many of the studies included in our review may reflect how retirement and possibilities to extend working have become a more urgent policy and research priority, accompanying the recognition of an ageing population with fewer people in work to support those in retirement.

In terms of limitations, the review was limited to non-disability retirement among employees who have not been identified as having ill health. Some of the included analyses were cross-sectional, and as such, participant characteristics could act as confounding variables that influence both the reporting of workplace psychosocial variables and the reporting of retirement intentions. Longitudinal studies of retirement behaviour demonstrate that perceived psychosocial factors at baseline are associated not only with retirement intentions but also with retirement behaviour at follow-up.

This is consistent with psychosocial factors being causally implicated in retirement decisions. Given the considerable heterogeneity between papers regarding definitions of psychosocial factors and methods of statistical analysis, our review did not include a meta-analysis. Another set of limitations arises from varying definitions of the retirement outcome in each study.


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Some studies defined retirement behaviours in relation to a specific time point e. Future research should seek to incorporate more precise, and consistent, measures of psychosocial work characteristics. With respect to job demands, there was some evidence that greater challenge at work promotes later retirement outcomes, but existing measures of demands probably conflate positive stimulating aspects of work with burdensome and effortful aspects of work.

Consistent with this theory, we formulate hypothesis 5: Hypothesis 5: Altruistic attitudes influence the intention to leave a bequest. The survey was carried out in two stages. In the first phase, data were collected using a non-probability sampling survey technique among Indians of differing demographics including gender, age, educational and social backgrounds during — Considering the sensitivity of the topic, the survey instrument was administered to only those who were willing to take the survey.

Thus, it was difficult to calculate a response rate based on the number of people who refused to respond. Two hundred and sixty-three individuals responded to the survey but only responses could be used for the analysis after accommodating the missing values. For its explanatory superiority in the Indian context, the final instrument used in this study was predominantly that from Wiepking et al. The questions were organized into several groups like altruistic attitudes, religious values, financial perception and social reputation were applied in the Australian setting by the researchers.

All the attributes were measured on the 5-point Likert scale. Socio-demographic questions were included and the survey instrument was vetted through an expert review process. The data obtained from the survey were analyzed using bivariate analysis. Of the total respondents, around half of the married respondents were seen to have an intention of leaving a bequest. About 76 percent of the respondents who had a bequest intention had not received an inheritance from their ancestors.

Table 1 shows the standardized summary statistics for all the continuous variables, grouped by the dependent variable, intention to make a bequest. The mean self-interest for households that do not intend to make a bequest is 0.

Interestingly, the mean religiosity of households that are not planning to make a bequest is 0. Further, the mean social norm for households willing to leave a bequest is 0. Table 1. Table 2 shows the correlations between the continuous variables. Social norms and religiosity have a significant positive correlation of 0. Further, none of the variables are found to have a significant negative relationship.

Table 2. The selfish life-cycle model of Modigliani and Brumberg x assumes that an average household is selfish and derives utility only from its own consumption. Further, we also explored the interaction effect of altruism on the relationship between self-interest and intention to make a bequest. The interaction effect shown in Figure 1 indicates that altruism negatively amplifies the relationship between self-interest and the intention to bequest.

Segmentation analysis of financial savings markets through the lens of psycho-demographics

More specifically, as self-interest increases by one unit, the probability of bequeathing is one-third of not bequeathing. Similarly, Figure 3 shows the interaction effect of religiosity on the relationship between self-interest and the bequest intention. For a one-unit increase in self-interest, the odds of leaving a bequest to the odds of not leaving a bequest is equal to 0.

Table 3. The scale variables are standardized. A bivariate analysis of the effect of social norms on intention to bequest suggests that a one-unit increase in social norms significantly increases the odds of leaving a bequest by a factor of 1. We also found that for a household with average self-interest, a one-unit increase in social norms doubles the odds of leaving a bequest. Similarly, for households with average altruism and religiosity, a one-unit increase in social norms increases the odds by a factor 1.

The interaction analysis of self-interest and altruism as shown in Figure 1 suggests that an increase in altruism amplifies the negative relationship between self-interest and intention to bequest. To elaborate further, our findings do not support the common theory that Indian households with high levels of altruism have a higher intention to bequest wealth. There may be many households that continue to imbibe the Indian culture of philanthropy and leave their wealth to help the less-fortunate outside the family. Similar to the moderating effect of altruism, we find evidence that social norms negatively amplify the relationship between self-interest and the intention to leave wealth.

Prosociality of households on an average is an important positive predictor of bequeathing. However, our results suggest that for households with higher levels of social norms, as self-interest increases probability to bequest decreases faster than for those with lower levels of prosociality. It could be because of the peculiar need of the families with the higher social status to leave a reputational legacy behind because of cultural norms prevailing in the country.

Religiosity is also seen to negatively amplify the relationship between self-interest and intention to bequest. More specifically, as in Figure 2 we found that among Indian households that are highly religiosity, self-interest increases the probability to bequeath decreases. India is no exception for its deep rooted philanthropic tradition, characteristics of which is still displayed largely motivated by religion.

With an objective to understand the factors that help in discriminating between the households with an intention to bequeath and those with no such intention, we conduct a series of t-tests. The results shown in Figure 4 indicate that self-interest of households with an intention to bequeath is significantly lower than those who do not have such an intention.

Social norms of households with an intention to leave a bequest is found to be higher than the rest. In contrast to Horioka , we find that the level of altruism is not significantly different for households with and without an intention to bequeath. Finally, the age of households with an intention to bequeath is significantly higher than those with no such intention. Figure 4. T-tests for Comparing Intention to Bequest. In this paper, we examine the relationship between household preferences and bequest intentions in an emerging market context, India. We find that, among the factors related to household preferences, self-interest has a negative association and social norms has a positive association with the intention to bequest.

Interestingly, altruism does not have a significant effect on the bequest intentions of Indian households. Further, we find that self-interest, altruism, social norms negatively amplifies the relationship between self-interest and bequest intention. The results would be useful for economists modelling household preferences, particularly in an emerging market context with relatively less matured social security systems and policies.

Economic and cultural contexts play a critical role in determining the impact of government policies on household behavior. Traditionally, in contrast to the developed economies, the savings behavior of Indian households have shown a bias towards physical assets such as real estate and gold. The sub-optimal savings behavior of the households poses several challenges to policy makers. For example, in , government abolished the inheritance tax, levied against the value of an asset during the time of its inheritance. Reinstatement of inheritance taxes for physical assets would stimulate the households, particularly the selfish households, to sell the less productive physical assets and increase their consumption spending.

This would also help alleviate wealth inequalities prevailing in Indian society. Further, pay-as-you-go is a system in which a person or organization pays for the costs of something when they occur rather than before or afterwards. Introduction of an effective pay- as- you —go pension systems would enable selfish households to spend more during their pre-retirement period, as they would be less concerned about the post-retirement life. This would improve their overall living standards. Finally, the reverse mortgage market in India is in the nascent stage of its development.

Conducive policies, exploiting the findings of our study, could potentially help in unlocking the substantial wealth trapped in the residential properties using innovative reverse mortgage products. You are free to: Share — copy and redistribute the material in any medium or format. Adapt — remix, transform, and build upon the material for any purpose, even commercially.

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To learn about our use of cookies and how you can manage your cookie settings, please see our cookie policy. In a context of a constant tax rate, the retirement age of workers in simple jobs remains unaffected. Finally, the positive impact of technological progress on the retirement age is lower the higher the generosity of the retirement system. We now turn to micro data to investigate empirically the impact of a technological change on the retirement intentions of workers depending on whether their productivity is indexed to the state of technology, that is, depending on whether workers are trained or not after a technological change.

Our intuition is close to Bartel and Sichermann and Friedberg In case of a technological change, workers are expected to advance their retirement date but only if they suffer a drop in their productivity. If their skills are updated, we expect that technical change encourages workers to delay their exit age. To overcome this problem, we proceed in the following way. We argue that workers benefiting from on-the-job training after a technological change will see their productivity indexed to the state of technology, whereas workers not concerned with a training program will suffer skill obsolescence.

This corresponds well to our theoretical framework according to which workers in complex positions see their productivity improved together with the state of technology, whereas workers in simple jobs suffer a skill obsolescence process. We combine individual data including information on the intended exit age of workers and aggregate data on both ICT adoption and participation to firm-sponsored training session. This data set corresponds to an ad hoc module of the annual French Labour Force survey conducted during that specific year and concerns all people aged from 50 to 69 years old so born between and Detailed demographic and economic characteristics of respondents are obtained by matching the TWR survey with the Labour Force survey.

Our main variable of interest is the intended retirement age. While some authors have focused on transitions from work to retirement see Behaghel et al. Several studies have shown the relevance of such self-reported indicators. Using the first two waves of the Health and Retirement Study, Dwyer and Hu have shown that among workers who planned at wave 1 to retire by wave 2, more than one half was effectively fully or partially retired the next wave.

Using the same dataset, Benitez-Silva and Dwyer conclude that retirement intentions are consistent with rational behavior. To provide some evidence on the reliability of retirement intentions for the French case, we have estimated robust correlation between the proportion of workers aged years old in and expecting to leave the labor market at 60 or after, computed at the industry-occupation level from the TWR survey, and the proportion of workers aged years old still employed five years later, also computed at the industry-occupation level using the French Labour Force survey.

We find a statistically significant correlation of 0. In Figure 1 , we represent the share of employed workers in the whole population of respondents by age. A simple strategy to avoid this selection bias consists in keeping the youngest workers only. Thus, we restrict our sample to working male respondents aged 55 years old or less and exclude self-employed workers. Also, we decide to focus on the retirement intentions of men only as the retirement behavior of women may be more affected by family considerations.

This leaves us with a sample of 1, male respondents. Our dependent variable is the age at which workers intend to leave the labor market. Respondents have to choose one of the three following categories in the TWR questionnaire: i before reaching 60 years old, ii between 60 and 64 years old, or iii after 64 years old. For each respondent, we have information on age, gender, marital status, health status, educational level, working in private or public sector, job tenure, full-time or part-time job, and monthly wage.

As noted by Hairault et al. In the TWR survey, individuals report the number of years they have contributed to the pension system so far. As in , the required number of contribution years to be entitled to a pension at the highest replacement rate is 40 years, the distance to the full pension age is obtained by subtracting the number of years individuals have already contributed from Given the French legislation in , we also account for the fact that older workers reaching 65 can be entitled to the highest replacement rate, even when they have not contributed during 40 years.

Next, we need some information on the way workers are affected by technological changes in their work environment and also on the probability that their skills are updated after such a shock. As far as we know, there is no individual survey that gathers information on retirement intentions, technical changes in the work environment and training participation.

As a few sectors are missing in this database, e. This is in line with our theoretical model in which the effect of a technological change on retirement intentions may vary between different types of jobs. An important issue here consists in the choice of the aggregation level. This implies a trade-off between having the finest decomposition and having a sufficient number of individuals in each industry-occupation cell.

In what follows, we define industries using the NAF classification and consider four occupations: managers, technicians, clerical workers and blue-collar workers. Our data was collected in , several years after the ICT boom. So simple indicators of ICT use do not correspond to a technological change. Therefore, we consider the probability for workers of having experienced a technical change in their work environment over the last three years.

Respondents were also asked whether they have participated over the last three years to a firm-sponsored training session on the use of new softwares or new computer devices. We use this information to define for each industry-occupation cell the proportion of workers having been trained.

This type of training relates to some specific skills that may become quickly obsolete after a technological change. While one week duration may seem very short, it is in line with the average duration of training sessions in France in When working with these aggregate variables, restricting our sample to older workers above 50 may lead to some selection bias.

Indeed, older workers who participate on a training session on new computer tools may intend to retire later because skill updating induces improved work opportunities.

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At the same time, employers may be tempted to invest in the skills of the employees who intend to postpone their retirement age reverse causality. Potentially, this simultaneity issue may also be problematic for our indicator of technological change. Retirement intentions of older workers may have been internalized by employers, therefore influencing their decision of adopting new technologies. To address this issue, we follow the approach of Friedberg by considering workers aged rather than workers aged 50 or more when constructing our aggregate indicators.

The underlying idea is that a high likelihood of skill updating among workers aged implies that the gains for the employer are higher than the training cost in this specific industry-occupation cell. The identifying assumption is to consider that the training incentives among workers aged years are not correlated with their retirement considerations. For a sake of robustness, we have also considered several other age groups further from retirement, in particular the years interval.

As our variable of technical change is self-reported by the worker, it may be subject to classical measurement errors. To ensure the validity of this indicator as a good proxy for measuring technical change, we exploit the information contained in the COI data at the employer-level.

More precisely, employers are asked about the introduction of some modern management tools and ICT equipment in their firm, at the time of the survey in and also three years before in Regarding ICT, we take 15 items into account and provide a description of these items in Table 3. We rely on indicators built by Bigi et al. However, while this variable is computed at the firm-level, it could be that the new ICT tools have been implemented for some type of jobs but not for others. Consequently, we could miss some information on the probability of technical change at the industry-occupation level.

This information, extracted from the employer-level survey, will be used to test the validity of this covariate at the industry level. We build our theoretical model on the main assumption that, in case of technological change, older workers occupying complex jobs receive training even though their working horizon is short.

However, these figures may be subject to some selection bias. Indeed, we have already shown in Figure 1 that after 55, the probability to remain employed falls dramatically. Using our training variable from the COI employee-level data and restricting our sample to workers aged , we investigate whether access to training may vary across jobs of different skill levels, holding the working horizon constant. We decompose workers into three groups: the first is close to the full pension age two years or less , the second is further from retirement between three and eight years from the full pension age and the third is too far from retirement nine years or more.

As shown in Figure 2 , the training rate is still high for managers, even for those who are at two years or less from the full pension age, and varies across skill levels. Proportion of trained males at different career horizons across skill levels. Note: The career horizon is defined as the difference between the full pension age and the age of the respondent. The full pension age is determined by the required number of contributive years to be entitled to a pension at the highest replacement rate.

Now we describe retirement intentions of French male workers in as well as their individual characteristics. Table 4 shows that, while When comparing the distributions of covariates in each column, we see that the distance to retirement is strongly positively correlated with intended exit age.

This is consistent with previous empirical findings of Hairault et al.

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Saving for Retirement: Intention, Context and Behavior - Semantic Scholar

As the distance to full pension age increases with the exit age from the schooling system, it is not surprising that the higher the educational level, the higher the intended retirement age. Our goal is to study the link between retirement intentions and some characteristics of the work environment affecting productivity, such as the frequency of technical change or the chance to receive firm-sponsored training, computed at the industry-occupation level.

We find a positive correlation equal to 0. While on average training rates equal Furthermore, there is a negative correlation equal to As shown in Table 4 , jobs occupied by individuals with high intended retirement age are on average less likely to be hit by a technical change than jobs occupied by workers willing to exit early.

This is consistent with previous findings of Bartel and Sichermann and Ahituv and Zeira So, we examine whether the effect of a technical change on retirement intentions of workers may depend on the way their productivity is indexed to the state of technology through on-the-job training. We investigate the effect of technical change computed at the industry level on retirement intentions of older workers.

Since the information on individual retirement intentions is measured by an ordered variable, we turn to an ordered Probit regression to explain the determinants of retirement intentions. We first include only a set of individual-specific characteristics described in Table 4.

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Then, we add the average probability of technical change in the regression. This variable is introduced in two ways. First, we consider the average probability, computed at the industry-level, that workers report having experienced a change in the techniques used over the last three years. We decompose this variable into quartiles and consider for each industry the proportion of workers whose employers report a high intensity of technical change.

To account for the correlation of observations at the industry level, we correct standard errors using a clustering procedure following Moulton Our results are reported in Table 5. First, we discuss briefly the coefficients obtained by regressing the ordered variable of intended exit age on our set of individual characteristics column 1. Since we introduce some indirect determinants of individual wage, like for instance educational level or occupation, we do not include the wage in the set of covariates to avoid potential multicollinearity issues.

Furthermore, the distance to full pension age exerts a strongly positive and significant effect on the intended exit age, which corresponds well to the horizon effect highlighted by Hairault et al. Then, we examine the effect of our industrial indicators of technical change on the intended exit age of respondents columns 2 and 3.

We obtain similar results whether we consider our variable of technical change from the employee data or the indicator directly reported by employers and consequently less subject to measurement error. We find that both variables have a negative and significant effect on the intended retirement age.

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As these variables relate to a technical change specific to the industry, our findings are consistent with the erosion effect highlighted by Ahituv and Zeira Next we estimate the same ordered regressions as before but consider aggregate variables at the industry-occupation levels. So, standard errors are now clusterized at the industry-occupation level. First, we study the effect of the average probability of participating to firm-sponsored training session on the use of new computer devices on retirement intentions. Recall that this probability is computed for workers aged , so it allows to remove a potential simultaneity bias.

Then, we investigate how technical change may interact with training to influence retirement intentions of workers. We report our results in Table 6. In column 1, we see that training encourages older workers to delay their retirement decisions. This finding is in line with previous work of Picchio and Van-Ours , who suggest additional on-the-job training to maintain older workers in employment or with the work of Behaghel et al. However, the role of training on retirement intentions turns out to be strongly driven by the interaction with technical change.

Indeed, in column 2, we see that the coefficient associated with the interaction term is strongly significant and positive while the effect of the probability of skill updating becomes non significant. At first sight, this result seems consistent with our theoretical predictions. In jobs with a high probability of skill updating, technical change may encourage workers to delay their intended exit age. However, as noted by Ai et al. The problem is even more complex in our case since the dependent variable is ordered and not binary.

To assess the role played by training, we decide to rely on the latent variable measuring the propensity to delay the retirement decision. The regression includes both the average probability of skill updating, the average probability of technical change and an interaction term crossing these two covariates.

As shown in column 3 of Table 6 , we find very comparable results for the ordered regression on the categorical retirement variable and for the OLS model estimated on the latent propensity to delay retirement. Using the method of simulated residuals, we can now interpret the interaction term in a straightforward way. To provide a graphical illustration, we plot the propensity to delay the retirement decision as a function of the average probability of technical change, setting the probability of skill updating to 0 simple jobs in one case and to 1 complex jobs in the other case.

The magnitude of the interaction term is determined by examining the difference in slopes between the two lines. We report using horizontal lines in Figure 3 the two threshold values obtained from the ordered Probit model column 2 of Table 6. Marginal effect of technical change on the propensity of older workers to delay their retirement decision.

Lecture: The latent outcome associated to the ordered intended retirement age with three categories is obtained by the methodology of simulated residuals. The lower horizontal dash-dotted line represents the threshold value of the latent outcome below which the respondents intend to leave their job before The upper horizontal dash-dotted line represents the threshold value of the latent outcome value of the latent outcome above which respondents intend to leave their job at 65 or after. Both thresholds are those from the ordered Probit regressions explaining intended retirement age, whose estimates are used when applying the method of simulated residuals.

The dashed decreasing line represents the latent outcome as a function of the average probability of technical change, computed at the industry-occupation level, in the case where the average training rate computed at the industry-occupation level among the workers aged years old, is set to 0. The solid increasing line stands for the latent outcome as a function of the average probability of technical change in the case where the average training rate is set to 1.

Figure 3 shows that the dashed line, corresponding to simple jobs, is decreasing with the average probability of technical change.

Are You Getting the Benefit of Your Employee Benefits?

So, in absence of training, technical change discourages older workers to continue their activity. While in simple jobs without technical change, workers may intend to exit the labour force between 60 and 64, a high probability of technical change leads these workers to advance their retirement intentions to less than 60 years old. The solid line in Figure 3 , corresponding to complex jobs, is increasing with the average probability of technical change.

So, in jobs in which productivity is indexed to the shift in the technological frontier, technical change may encourage older workers to retire later. For complex jobs, the erosion effect of technical change is not only mitigated by training but is rather reversed. In some stable work environment low probability of technical change , the intended exit age ranges from 60 to